FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.
arXiv preprint arXiv:2409.11585 , year=
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PASE is a neuro-symbolic self-healing system that synthesizes LLM recovery plans, verifies them in simulation, and uses DRL to optimize prompts, claiming over 40% faster recovery on cloud fault data.
citing papers explorer
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FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.
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Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
PASE is a neuro-symbolic self-healing system that synthesizes LLM recovery plans, verifies them in simulation, and uses DRL to optimize prompts, claiming over 40% faster recovery on cloud fault data.